A Systematic Approach to Portfolio Optimization: A Comparative Study of Reinforcement Learning Agents, Market Signals, and Investment Horizons

A Systematic Approach to Portfolio Optimization: A Comparative Study of Reinforcement Learning Agents, Market Signals, and Investment Horizons

This paper presents a systematic exploration of deep reinforcement learning (RL) for portfolio optimization and compares various agent architectures, such as the DQN, DDPG, PPO, and SAC. We evaluate these agents’ performance across multiple market signals, including OHLC price data and technical ind...

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Bibliographic Details
Main Authors: Francisco Espiga-Fernández, Álvaro García-Sánchez, Joaquín Ordieres-Meré
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Algorithms
Subjects:
reinforcement learning
portfolio optimization
policy optimization
deep learning
deep reinforcement learning
mean-variance optimization
Online Access:https://www.mdpi.com/1999-4893/17/12/570

Internet

https://www.mdpi.com/1999-4893/17/12/570

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